Multi-Agent Reinforcement Learning

…various species, but studying complex multi-agent dynamics and accompanying neural activity in natural settings remains challenging. Here we present an in silico study of social interaction during foraging using recurrent neural network (RNN) agents trained using multi-agent reinforcement learning (MARL). Our framework explores how different group sizes, food availability, and sensory capabilities shape emergent foraging strategies in grops of 2 to 10 agents. These group sizes are especially relevant to experimental social ne

…of the basic structure of social learning. In several studies, we found that initial negative interactions with some members of a group can cause subsequent avoidance of the entire group, and that this avoidance perpetuates stereotypes. Additional cognitive modeling discovered that approach and avoidance behavior based on biased beliefs not only influences the evaluative (positive or negative) impressions of group members, but also shapes the depth of the cognitive

…Using multi-agent reinforcement

Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at clima